from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-20 14:08:23.228259
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 20, Nov, 2020
Time: 14:08:27
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.3270
Nobs: 116.000 HQIC: -43.5961
Log likelihood: 1187.50 FPE: 4.91449e-20
AIC: -44.4634 Det(Omega_mle): 2.33489e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.738765 0.218211 3.386 0.001
L1.Burgenland 0.133130 0.093222 1.428 0.153
L1.Kärnten -0.315548 0.078258 -4.032 0.000
L1.Niederösterreich 0.002454 0.225386 0.011 0.991
L1.Oberösterreich 0.270906 0.182119 1.488 0.137
L1.Salzburg 0.128783 0.091859 1.402 0.161
L1.Steiermark 0.079494 0.130131 0.611 0.541
L1.Tirol 0.164000 0.086044 1.906 0.057
L1.Vorarlberg 0.006837 0.086289 0.079 0.937
L1.Wien -0.171640 0.176058 -0.975 0.330
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.759794 0.279702 2.716 0.007
L1.Burgenland -0.023966 0.119491 -0.201 0.841
L1.Kärnten 0.345507 0.100311 3.444 0.001
L1.Niederösterreich 0.061546 0.288898 0.213 0.831
L1.Oberösterreich -0.216231 0.233438 -0.926 0.354
L1.Salzburg 0.160505 0.117744 1.363 0.173
L1.Steiermark 0.191207 0.166801 1.146 0.252
L1.Tirol 0.140865 0.110291 1.277 0.202
L1.Vorarlberg 0.187026 0.110604 1.691 0.091
L1.Wien -0.572988 0.225671 -2.539 0.011
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.356842 0.092265 3.868 0.000
L1.Burgenland 0.104132 0.039417 2.642 0.008
L1.Kärnten -0.023913 0.033089 -0.723 0.470
L1.Niederösterreich 0.128307 0.095299 1.346 0.178
L1.Oberösterreich 0.262521 0.077004 3.409 0.001
L1.Salzburg -0.000587 0.038840 -0.015 0.988
L1.Steiermark -0.064142 0.055023 -1.166 0.244
L1.Tirol 0.094582 0.036382 2.600 0.009
L1.Vorarlberg 0.147898 0.036485 4.054 0.000
L1.Wien 0.004766 0.074442 0.064 0.949
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.209300 0.111135 1.883 0.060
L1.Burgenland 0.003783 0.047478 0.080 0.937
L1.Kärnten 0.037305 0.039857 0.936 0.349
L1.Niederösterreich 0.090383 0.114789 0.787 0.431
L1.Oberösterreich 0.346544 0.092753 3.736 0.000
L1.Salzburg 0.094447 0.046784 2.019 0.044
L1.Steiermark 0.194890 0.066276 2.941 0.003
L1.Tirol 0.025546 0.043822 0.583 0.560
L1.Vorarlberg 0.113521 0.043947 2.583 0.010
L1.Wien -0.115737 0.089667 -1.291 0.197
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.901718 0.238794 3.776 0.000
L1.Burgenland 0.034481 0.102015 0.338 0.735
L1.Kärnten -0.012628 0.085640 -0.147 0.883
L1.Niederösterreich -0.141703 0.246646 -0.575 0.566
L1.Oberösterreich 0.044702 0.199297 0.224 0.823
L1.Salzburg 0.056072 0.100524 0.558 0.577
L1.Steiermark 0.114085 0.142406 0.801 0.423
L1.Tirol 0.237713 0.094160 2.525 0.012
L1.Vorarlberg 0.025228 0.094428 0.267 0.789
L1.Wien -0.224485 0.192666 -1.165 0.244
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.197688 0.166077 1.190 0.234
L1.Burgenland -0.042784 0.070950 -0.603 0.546
L1.Kärnten -0.011395 0.059561 -0.191 0.848
L1.Niederösterreich 0.205805 0.171538 1.200 0.230
L1.Oberösterreich 0.395470 0.138608 2.853 0.004
L1.Salzburg -0.037632 0.069912 -0.538 0.590
L1.Steiermark -0.056166 0.099041 -0.567 0.571
L1.Tirol 0.197296 0.065487 3.013 0.003
L1.Vorarlberg 0.052255 0.065673 0.796 0.426
L1.Wien 0.110768 0.133995 0.827 0.408
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.353368 0.210927 1.675 0.094
L1.Burgenland 0.055327 0.090110 0.614 0.539
L1.Kärnten -0.082727 0.075646 -1.094 0.274
L1.Niederösterreich -0.149432 0.217862 -0.686 0.493
L1.Oberösterreich -0.122904 0.176039 -0.698 0.485
L1.Salzburg -0.001555 0.088792 -0.018 0.986
L1.Steiermark 0.384586 0.125787 3.057 0.002
L1.Tirol 0.541108 0.083172 6.506 0.000
L1.Vorarlberg 0.216668 0.083408 2.598 0.009
L1.Wien -0.181498 0.170181 -1.067 0.286
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.232187 0.242146 0.959 0.338
L1.Burgenland 0.005112 0.103447 0.049 0.961
L1.Kärnten -0.072848 0.086842 -0.839 0.402
L1.Niederösterreich 0.208742 0.250107 0.835 0.404
L1.Oberösterreich 0.011893 0.202094 0.059 0.953
L1.Salzburg 0.233064 0.101935 2.286 0.022
L1.Steiermark 0.154896 0.144404 1.073 0.283
L1.Tirol 0.055926 0.095482 0.586 0.558
L1.Vorarlberg -0.004878 0.095753 -0.051 0.959
L1.Wien 0.194775 0.195369 0.997 0.319
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.698822 0.133949 5.217 0.000
L1.Burgenland -0.014644 0.057225 -0.256 0.798
L1.Kärnten -0.014674 0.048039 -0.305 0.760
L1.Niederösterreich -0.076837 0.138354 -0.555 0.579
L1.Oberösterreich 0.266067 0.111794 2.380 0.017
L1.Salzburg 0.004418 0.056388 0.078 0.938
L1.Steiermark 0.006404 0.079881 0.080 0.936
L1.Tirol 0.080545 0.052818 1.525 0.127
L1.Vorarlberg 0.177293 0.052969 3.347 0.001
L1.Wien -0.112846 0.108074 -1.044 0.296
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.082990 -0.078744 0.194174 0.243454 0.014019 0.069776 -0.147616 0.086686
Kärnten 0.082990 1.000000 -0.074787 0.176041 0.054530 -0.164791 0.170795 0.001216 0.277598
Niederösterreich -0.078744 -0.074787 1.000000 0.209754 0.029677 0.146094 0.061295 0.030896 0.350038
Oberösterreich 0.194174 0.176041 0.209754 1.000000 0.234355 0.261895 0.066080 0.050680 0.027664
Salzburg 0.243454 0.054530 0.029677 0.234355 1.000000 0.135665 0.033114 0.057168 -0.076518
Steiermark 0.014019 -0.164791 0.146094 0.261895 0.135665 1.000000 0.092939 0.094156 -0.209099
Tirol 0.069776 0.170795 0.061295 0.066080 0.033114 0.092939 1.000000 0.128797 0.081360
Vorarlberg -0.147616 0.001216 0.030896 0.050680 0.057168 0.094156 0.128797 1.000000 0.061414
Wien 0.086686 0.277598 0.350038 0.027664 -0.076518 -0.209099 0.081360 0.061414 1.000000